{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "036d48de-a702-4550-9c1c-886c26f948b0",
   "metadata": {},
   "source": [
    "# <span style=\"color: #9333EA;\">复合柱形图</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f0be19d-51e9-4526-818a-0f2cc756fecb",
   "metadata": {},
   "source": [
    "### 使用筛选好的衬衫订单数量（2024年11月-2025年10月）绘制复合柱形图"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a50137a1-79c4-4171-bbfd-0e7555765e0c",
   "metadata": {},
   "source": [
    "### 2024年11月-2025年10月衬衫订单月度销量统计\n",
    "| 年月   | 订单数量 | 总销量 |\n",
    "|--------|----------|--------|\n",
    "| 2024-11 | 5        | 11     |\n",
    "| 2024-12 | 14       | 44     |\n",
    "| 2025-01 | 10       | 26     |\n",
    "| 2025-02 | 14       | 40     |\n",
    "| 2025-03 | 10       | 26     |\n",
    "| 2025-04 | 12       | 41     |\n",
    "| 2025-05 | 12       | 34     |\n",
    "| 2025-06 | 13       | 38     |\n",
    "| 2025-07 | 12       | 40     |\n",
    "| 2025-08 | 16       | 44     |\n",
    "| 2025-09 | 6        | 20     |\n",
    "| 2025-10 | 4        | 14     |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cc63e2b4-e130-4839-b4a6-cc9ea505f44c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据预览：\n",
      "         年月  订单数量\n",
      "0   2024-11     5\n",
      "1   2024-12    14\n",
      "2   2025-01    10\n",
      "3   2025-02    14\n",
      "4   2025-03    10\n",
      "5   2025-04    12\n",
      "6   2025-05    12\n",
      "7   2025-06    13\n",
      "8   2025-07    12\n",
      "9   2025-08    16\n",
      "10  2025-09     6\n",
      "11  2025-10     4\n",
      "季度总和数据：\n",
      "              季度  订单数量  月份数\n",
      "0  2024Q4-2025Q1    29    3\n",
      "1      2025Q1-Q2    48    4\n",
      "2      2025Q3-Q4    51    5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.patches import Patch\n",
    "\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'Noto Sans CJK JP']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 使用图片中的数据\n",
    "data = {\n",
    "    '年月': ['2024-11', '2024-12', '2025-01', '2025-02', '2025-03', '2025-04', \n",
    "           '2025-05', '2025-06', '2025-07', '2025-08', '2025-09', '2025-10'],\n",
    "    '订单数量': [5, 14, 10, 14, 10, 12, 12, 13, 12, 16, 6, 4]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "print(\"数据预览：\")\n",
    "print(df)\n",
    "# 计算季度分组\n",
    "quarters = {\n",
    "    '2024Q4-2025Q1': ['2024-11', '2024-12', '2025-01'],\n",
    "    '2025Q1-Q2': ['2025-02', '2025-03', '2025-04', '2025-05'],\n",
    "    '2025Q3-Q4': ['2025-06', '2025-07', '2025-08', '2025-09', '2025-10']\n",
    "}\n",
    "\n",
    "# 计算每个季度的总和\n",
    "quarterly_data = []\n",
    "for quarter, months in quarters.items():\n",
    "    # 筛选该季度的数据\n",
    "    quarter_df = df[df['年月'].isin(months)]\n",
    "    # 计算总和\n",
    "    order_sum = quarter_df['订单数量'].sum()\n",
    "    quarterly_data.append({\n",
    "        '季度': quarter,\n",
    "        '订单数量': order_sum,\n",
    "        '月份数': len(months)\n",
    "    })\n",
    "\n",
    "quarterly_df = pd.DataFrame(quarterly_data)\n",
    "print(\"季度总和数据：\")\n",
    "print(quarterly_df)\n",
    "# 创建与图1相同样式的图表\n",
    "fig, ax = plt.subplots(figsize=(16, 10))\n",
    "\n",
    "# 设置深蓝色背景\n",
    "fig.patch.set_facecolor('#1a1a3b')\n",
    "ax.set_facecolor('#1a1a3b')\n",
    "\n",
    "# 设置柱状图的位置和宽度\n",
    "x = np.arange(len(df['年月']))\n",
    "width = 0.25\n",
    "\n",
    "# 定义颜色（模仿图1的配色方案）\n",
    "colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FECA57', '#FF9FF3']\n",
    "\n",
    "# 绘制每月订单数量的柱子\n",
    "bars1 = ax.bar(x, df['订单数量'], width*1.5, \n",
    "               color=colors[0], alpha=0.9, label='订单数量')\n",
    "\n",
    "# 绘制季度总和柱子\n",
    "quarter_groups = [\n",
    "    {'name': '2024Q4-2025Q1', 'months': ['2024-11', '2024-12', '2025-01'], 'color': colors[2]},\n",
    "    {'name': '2025Q1-Q2', 'months': ['2025-02', '2025-03', '2025-04', '2025-05'], 'color': colors[3]},\n",
    "    {'name': '2025Q3-Q4', 'months': ['2025-06', '2025-07', '2025-08', '2025-09', '2025-10'], 'color': colors[4]}\n",
    "]\n",
    "\n",
    "# 绘制季度总和柱子\n",
    "quarter_bars = []\n",
    "for i, quarter in enumerate(quarter_groups):\n",
    "    quarter_data = df[df['年月'].isin(quarter['months'])]\n",
    "    order_sum = quarter_data['订单数量'].sum()\n",
    "    \n",
    "    # 计算季度柱子的位置（跨越该季度所有月份）\n",
    "    start_idx = df[df['年月'] == quarter['months'][0]].index[0]\n",
    "    end_idx = df[df['年月'] == quarter['months'][-1]].index[0]\n",
    "    center_pos = (start_idx + end_idx) / 2\n",
    "    quarter_width = (end_idx - start_idx) + 6\n",
    "    \n",
    "    # 绘制季度总和柱子\n",
    "    bar = ax.bar(center_pos, order_sum, quarter_width * width * 1.2, \n",
    "                color=quarter['color'], alpha=0.7, label=f'{quarter[\"name\"]}总和')\n",
    "    quarter_bars.append(bar)\n",
    "    \n",
    "    # 添加季度总和数值标签\n",
    "    ax.text(center_pos, order_sum + 1, f'{order_sum}', \n",
    "            ha='center', va='bottom', fontsize=12, color='white', fontweight='bold')\n",
    "\n",
    "# 设置标题和副标题（模仿图1的样式）\n",
    "ax.set_title('2024-2025年各月订单走势', \n",
    "            fontsize=20, color='white', pad=20, fontweight='bold')\n",
    "\n",
    "# 找出订单最多的月份和季度\n",
    "max_month_idx = df['订单数量'].idxmax()\n",
    "max_month = df.loc[max_month_idx, '年月']\n",
    "max_month_orders = df.loc[max_month_idx, '订单数量']\n",
    "\n",
    "max_quarter = max(quarter_groups, key=lambda x: df[df['年月'].isin(x['months'])]['订单数量'].sum())\n",
    "max_quarter_orders = df[df['年月'].isin(max_quarter['months'])]['订单数量'].sum()\n",
    "\n",
    "# 添加副标题\n",
    "ax.text(0.5, 0.95, f'{max_quarter[\"name\"]}订单最多{max_quarter_orders}，{max_month}单月订单最大{max_month_orders}', \n",
    "        transform=ax.transAxes, ha='center', va='top', fontsize=12, color='white', \n",
    "        style='italic')\n",
    "\n",
    "# 设置坐标轴标签\n",
    "ax.set_xlabel('年月', fontsize=12, color='white', labelpad=10)\n",
    "ax.set_ylabel('订单数量', fontsize=12, color='white', labelpad=10)\n",
    "\n",
    "# 设置x轴刻度\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(df['年月'], rotation=45, color='white')\n",
    "\n",
    "# 设置y轴标签颜色\n",
    "ax.tick_params(axis='y', colors='white')\n",
    "\n",
    "# 设置网格线\n",
    "ax.grid(True, alpha=0.2, color='white')\n",
    "\n",
    "# 在柱子上添加数值标签\n",
    "for bar in bars1:\n",
    "    height = bar.get_height()\n",
    "    if height > 0:\n",
    "        ax.text(bar.get_x() + bar.get_width()/2, height + 0.5, f'{int(height)}',\n",
    "                ha='center', va='bottom', fontsize=10, color='white')\n",
    "\n",
    "# 创建自定义图例\n",
    "legend_elements = [\n",
    "    Patch(facecolor=colors[0], label='订单数量'),\n",
    "    Patch(facecolor=colors[2], label='2024Q4-2025Q1总和'),\n",
    "    Patch(facecolor=colors[3], label='2025Q1-Q2总和'),\n",
    "    Patch(facecolor=colors[4], label='2025Q3-Q4总和')\n",
    "]\n",
    "\n",
    "ax.legend(handles=legend_elements, loc='upper right', facecolor='#1a1a3b', \n",
    "          edgecolor='white', labelcolor='white')\n",
    "\n",
    "# 设置坐标轴边框颜色\n",
    "ax.spines['bottom'].set_color('white')\n",
    "ax.spines['top'].set_color('white')\n",
    "ax.spines['right'].set_color('white')\n",
    "ax.spines['left'].set_color('white')\n",
    "\n",
    "# 调整布局\n",
    "plt.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "281efeb3-c49c-4d4f-89d0-27b58b80885f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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